It　is　known　that　Logistic　Regression　coupled　with　Partial　Least　Squares　dimension　reduction　(PLSDR-LD)　is　capable　of　extracting　a　great　deal　of　useful　information　for　classification　from　gene　expression　profile　and　getting　a　rather　high　classification　accuracy　rate.　In　this　study,　we　replace　the　logistic　function　of　Logistic　Regression　with　several　functions　which　are　similar　to　logistic　function　in　appearance,　and　apply　these　functions　to　the　analysis　of　microarray　data　sets　from　two　cancer　gene　expression　studies.　We　compare　these　newly　introduced　models　with　PLSDR-LD　proposed　in　the　literature.　The　most　effective　models　with　good　prediction　precision　are　lastly　provided　through　analyzing　the　results　of　two　experiments.　©2010　IEEE.